Intelligent Surgical Marker

ABSTRACT

Surgical marker systems and methods for delineating a lesion margin of a subject are provided. An example system includes a handheld probe device configured to capture an optical coherence tomography (OCT) image and a processor coupled to a memory. The handheld probe device includes a handheld probe including a fiber-optic probe assembly and a marker assembly. The processor is configured to: segment, by a neural network, each pixels of the OCT into different tissue-type categories; generate one or more feature vectors based at least in part on the segmented pixels; determine, by a one-class classifier, a boundary location in the OCT image between a normal tissue and an abnormal tissue of the tissue structure; and control the marker assembly to selectively create a visible label on a tissue location of the subject, the tissue location corresponding to the boundary location.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.63/216,142 filed on Jun. 29, 2021, the entire content of which is herebyincorporated by reference in its entirety.

BACKGROUND

Medical imaging techniques have been widely used to guide surgicalprocedures. Cross-sectional images taken before, during, and aftersurgery can provide information to develop a surgical plan, execute thesurgical procedure, and evaluate the surgical outcome. However, medicalimaging modalities, such as X-ray computed tomography (CT), magneticresonance imaging (MRI), and ultrasound imaging, have macroscopic ormesoscale resolution, and do not have sufficiently high spatialresolution to reveal microscopic morphological features associated withskin pathology.

Nonmelanoma skin cancers (NMSCs) are the most common cancers in theUnited States. In 2012, the total number of NMSCs in the US wasestimated to be more than 5.4 million and the number of patientsreceiving NMSC treatment was approximately 3.3 million. The number ofnewly diagnosed NMSC cases is projected to increase each year. Despiteits prevalence, NMSCs have relatively small mortality if treated early.

Mohs micrographic surgery (MMS) is used widely to treat skin cancers.MMS provides the highest cure rates for NMSCs. From 1995 to 2009, theuse of MMS increased by 400%, and currently 1 in 4 skin cancers is beingtreated with MMS. A major disadvantage of MMS is the long time needed toaccomplish this surgical procedure. MMS on average takes one to twohours or longer, because the subsurface malignancy of NMSC often extendsbeyond the visible tumor margin identified in the initial clinicalassessment. MMS is usually accomplished with multiple tissue excisionstages, each followed by histological examination. Accordingly, there isa clinical need for image guidance of MMS and other types of surgeries.

SUMMARY

Described herein are systems and methods for delineating a lesion margin(e.g., skin lesion margin, a tumor margin, or the like) of a subject(e.g., a patient). In accordance with example embodiments of thedisclosure, a surgical marker system is disclosed. The surgical markersystem can be an optical coherence tomography (OCT) integrated surgicalguidance platform. The surgical marker system can include a handheldprobe device configured to capture one or more optical coherencetomography (OCT) images. The OCT image provides an in-depth crosssectional view of a tissue structure (e.g., a skin structure) beneath atissue surface (e.g., a skin surface). The handheld probe deviceincludes a handheld probe including a fiber-optic probe assembly and amarker assembly. The fiber-optic probe assembly is configured to directlow-coherence light to a region of interest and collect light reflectedfrom the region of interest to acquire the OCT image(s). The markerassembly is configured to selectively create a visible label on thelesion margin of the subject. The surgical marker system can furtherinclude a processor coupled to a memory (e.g., a computer). Theprocessor is configured to segment, by a neural network (e.g., U-Netneural network), each pixel of the OCT image into different tissue-typecategories (e.g., a stratum corneum category, an epidermis category, anda dermis category); generate one or more feature vectors based at leastin part on the segmented pixels; determine, by a one-class classifier(e.g., one-class support vector machine (SVM) classifier), a boundarylocation between a normal tissue and an abnormal tissue of the tissuestructure based at least in part on the one or more feature vectors; andcontrol the marker assembly to selectively create the visible label on atissue location of the subject, the tissue location corresponding to theboundary location.

In accordance with other example embodiments of the disclosure, a methodis disclosed for delineating a lesion margin of a subject. The methodcan include capturing one or more OCT images using low-coherence light.The OCT image provides an in-depth cross sectional view of a tissuestructure beneath a tissue surface. The method can include segmenting,by a neural network, each pixel of the OCT image into differenttissue-type categories. The method can include generating one or morefeature vectors based at least in part on the segmented pixels. Themethod can determine, by a one-class classifier, a boundary location inthe OCT image between a normal tissue and an abnormal tissue of thetissue structure based at least in part on the one or more featurevectors for each of the one or more OCT images. The method can furtherinclude controlling a marker assembly to selectively create the visiblelabel on a tissue location of the subject, the tissue locationcorresponding to the boundary location.

Embodiments of the present disclosure can enable quantitative,objective, and data driven delineation of lesion margin and guide asurgeon to surgically remove lesions with higher accuracy compared toconventional systems and methods. The margin for tissue excision hasbeen conventionally determined by the surgeon, following visualinspection that is qualitative, subjective, and largely dependent on thesurgeon's training and experience. In contrast to the conventionalsystems and methods, the systems and methods of the present disclosureadvantageously provide for contemporaneous and concurrent margindetection and marking that can achieve more accurate tissue excision(particularly at the first stage) and reduce the time required by usinga single fiber OCT instrument that performs in vivo skin imaging andusing machine learning for tumor boundary assessment and marking,leading to more accurate tumor margin detection.

It is not a trivial task to determine the condition of the skin byvisually examining the OCT image. In clinical settings, an experiencedreader who can interpret OCT data accurately is usually not available.Moreover, the results of visual inspection depend on the reader'straining in dermatology and pathology, and can vary significantly. Toextract clinically relevant information to guide the surgery, thechallenge also comes from the fact that pathological features in OCTimages are often obscured by speckle noise and depth dependent signaldecay. To address the clinical need for accurate skin tissuecharacterization, the systems and methods taught herein provide a robustmachine learning method that analyzes OCT images and performs automaticskin tissue classification, and further provide an approach to extractfeatures learned by training a deep convolutional neural network (CNN)with a U-Net architecture, and to use the features to train theclassifier to perform one-class SVM classification for anomalydetection. Compared to manually selected features in conventionalmethods, CNN features are extracted automatically at different abstractlayers, and have the capability to provide a more objective andcomprehensive characterization for the tissue.

It is also challenging to get a comprehensive training data setrepresenting normal and abnormal skin tissue having various types,stages and grades of tumors. To overcome this challenge, the systems andmethods taught herein train a one-class classifier to recognize normalskin tissue using OCT data obtained from healthy subjects. Theclassifier is able to detect the skin tumor as an anomaly regardless ofcancer type, stage and grade.

Any combination and/or permutation of the embodiments is envisioned.Other objects and features will become apparent from the followingdetailed description considered in conjunction with the accompanyingdrawings. It is to be understood, however, that the drawings aredesigned as an illustration only and not as a definition of the limitsof the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee. To assist those of skill in the art in making andusing the disclosed systems and methods, reference is made to theaccompanying figures, wherein:

FIG. 1 is a diagram illustrating an example embodiment of a surgicalmarker system of the present disclosure.

FIG. 2A illustrates an example embodiment of a surgical marker system inaccordance with embodiments of the present disclosure.

FIG. 2B illustrates a handheld probe in FIG. 2A interacting with atissue in accordance with embodiments of the present disclosure.

FIG. 2C illustrates an example exterior of the handheld probe in FIG. 2Ain accordance with embodiments of the present disclosure.

FIG. 2D is a schematic view of an example embodiment of the handheldprobe in FIG. 2A.

FIG. 3A illustrates the handheld probe in FIG. 2A scanning a lesion areain accordance with embodiments of the present disclosure.

FIG. 3B illustrates a margin of a lesion labeled by the handheld probein FIG. 2A via various scans in accordance with embodiments of thepresent disclosure.

FIG. 4A schematically illustrates example scanning trajectories along atumor, a surgical margin, and a normal skin.

FIG. 4B is a clinical photograph of a basal cell carcinoma (BCC) tumoron a right neck imaged in a patient.

FIG. 4C is an OCT image of the BCC tumor in FIG. 4B using a firstscanning trajectory (12 o'clock direction) in FIG. 4A.

FIG. 4D is an OCT image of the BCC tumor in FIG. 4B using a secondscanning trajectory (3 o'clock direction) in FIG. 4A.

FIG. 4E is an OCT image of the BCC tumor in FIG. 4B using a thirdscanning trajectory (6 o'clock direction) in FIG. 4A.

FIG. 4F is an OCT image of the BCC tumor in FIG. 4B using a fourthscanning trajectory (9 o'clock direction) in FIG. 4A.

FIG. 4G is a clinical photograph of a squamous cell carcinoma (SCC)tumor on a scalp imaged in a patient.

FIG. 4H is an OCT image of the SCC tumor in FIG. 4G using a firstscanning trajectory (12 o'clock direction) in FIG. 4A.

FIG. 4I is an OCT image of the SCC tumor in FIG. 4G using a secondscanning trajectory (3 o'clock direction) in FIG. 4A.

FIG. 4J is an OCT image of the SCC tumor in FIG. 4G using a thirdscanning trajectory (6 o'clock direction) in FIG. 4A.

FIG. 4K is an OCT image of the SCC tumor in FIG. 4G using a fourthscanning trajectory (9 o'clock direction) in FIG. 4A.

FIG. 4L is a magnified illustration of a normal tissue area in FIG. 4C.

FIG. 5 is a flowchart illustrating overall processing steps carried outby the system of the present disclosure.

FIG. 6 illustrates a U-Net architecture for dermal OCT imagesegmentation.

FIG. 7A is an OCT image of a skin tissue.

FIG. 7B is a ground truth image providing ground truth labels for theOCT image in FIG. 7A.

FIG. 7C is a segmented OCT image having labels generated by the U-Netarchitecture in FIG. 6 for individual pixels of the OCT image in FIG.7A.

FIG. 8A is a photograph of a scar induced by laser irradiation.

FIG. 8B is an OCT image of the scar in FIG. 8A using a first scanningtrajectory (12 o'clock direction) in FIG. 4A.

FIG. 8C is a U-Net segmentation image of the OCT image in FIG. 8B.

FIG. 8D illustrates an epidermal thickness along the lateral dimensionof the OCT image in FIG. 8B.

FIG. 8E is an OCT image of the scar in FIG. 8A using a second scanningtrajectory (3 o'clock direction) in FIG. 4A.

FIG. 8F is a U-Net segmentation image of the OCT image in FIG. 8E.

FIG. 8G illustrates an epidermal thickness along the lateral dimensionof the OCT image in FIG. 8E.

FIG. 8H is an OCT image of the scar in FIG. 8A using a third scanningtrajectory (6 o'clock direction) in FIG. 4A.

FIG. 8I is a U-Net segmentation image of the OCT image in FIG. 8H.

FIG. 8J illustrates an epidermal thickness along the lateral dimensionof the OCT image in FIG. 8H.

FIG. 8K is an OCT image of the scar in FIG. 8A using a fourth scanningtrajectory (9 o'clock direction) in FIG. 4A.

FIG. 8L is a U-Net segmentation image of the OCT image in FIG. 8K.

FIG. 8M illustrates an epidermal thickness along the lateral dimensionof the OCT image in FIG. 8K.

FIG. 9A is a photograph of a SCC tumor at a forehead of a patient.

FIG. 9B is an OCT image of the SCC tumor in FIG. 9A using a firstscanning trajectory (12 o'clock direction) in FIG. 4A.

FIG. 9C is a U-Net segmentation image of the OCT image in FIG. 9B.

FIG. 9D illustrates an epidermal thickness along the lateral dimensionof the OCT image in FIG. 9B.

FIG. 9E is an OCT image of the SCC tumor in FIG. 9A using a secondscanning trajectory (3 o'clock direction) in FIG. 4A.

FIG. 9F is a U-Net segmentation image of the OCT image in FIG. 9E.

FIG. 9G illustrates an epidermal thickness along the lateral dimensionof the OCT image in FIG. 9E.

FIG. 9H is an OCT image of the SCC tumor in FIG. 9A using a thirdscanning trajectory (6 o'clock direction) in FIG. 4A.

FIG. 9I is a U-Net segmentation image of the OCT image in FIG. 9H.

FIG. 9J illustrates an epidermal thickness along the lateral dimensionof the OCT image in FIG. 9H.

FIG. 9K is an OCT image of the SCC tumor in FIG. 9A using a fourthscanning trajectory (9 o'clock direction) in FIG. 4A.

FIG. 9L is a U-Net segmentation image of the OCT image in FIG. 9K.

FIG. 9M illustrates an epidermal thickness along the lateral dimensionof the OCT image in FIG. 9K.

FIG. 10 is an example flowchart illustrating steps 1000 for training apre-trained one-class classifier to differentiate abnormal tissue fromnormal skin tissue in accordance with embodiments of the presentdisclosure.

FIG. 11 illustrates an activation value at each pixel corresponding toeach filter in accordance with embodiments of the present disclosure.

FIG. 12A is an example data flow for training a one-class classifier inaccordance with embodiments of the present disclosure.

FIG. 12B is an example data flow for applying the trained one-classclassifier from FIG. 12A to an incoming OCT image.

FIG. 13 is a graphical depiction showing an identification of supportvectors to detect outlier and skin abnormity using a combination ofepidermal thickness and the standard variation of epidermal thickness.

FIG. 14A is an OCT image obtained by scanning a fiber-optic probe acrossa junction between skin and a nail plate from a healthy subject.

FIG. 14B is a ground truth labeling of normal skin and nail plate(considered as abnormal skin).

FIG. 14C is an image showing features extracted from dermis pixels atdifferent spatial locations.

FIG. 14D illustrates a plot of prediction score outputs from the trainedSVM before and after low-pass filtering.

FIG. 14E is an image of abnormal skin identified by a one-classclassifier without filtering the prediction scores.

FIG. 14F is an image abnormal skin identified by the one-classclassifier after filtering the prediction scores.

FIG. 15 illustrates receiver operating characteristic (ROC) curves fordifferent classifiers obtained using a validating data set with normalskin images and computer synthesized abnormal images.

FIGS. 16A and 16B are OCT images of normal tissue from a patient havinga basal cell carcinoma tumor.

FIG. 16C schematically illustrates scanning trajectories for scanning atumor of the same patient in FIG. 16A.

FIG. 16D is an OCT image by scanning a tumor of the same patient in FIG.16A along a first scanning trajectory in FIG. 16C.

FIG. 16E is an OCT image by scanning the tumor along a second scanningtrajectory in FIG. 16C.

FIG. 16F is an OCT image by scanning the tumor in FIG. 16A along a thirdscanning trajectory in FIG. 16C.

FIG. 16G is an OCT image by scanning the tumor along a fourth scanningtrajectory in FIG. 16C.

FIG. 16H is an OCT image by scanning a tissue region along a fifthscanning trajectory in FIG. 16C.

FIG. 16I is an OCT image by scanning the tissue region along a sixthscanning trajectory in FIG. 16C.

FIG. 16J is an OCT image by scanning the tissue region along a seventhscanning trajectory in FIG. 16C.

FIG. 16K is an OCT image by scanning tissue region along an eighthscanning trajectory in FIG. 16C.

FIG. 17 is an histologic image of scanning areas in FIGS. 16D-16K.

FIG. 18 an example diagram illustrating computer hardware and networkcomponents on which the system can be implemented.

FIG. 19 is an example block diagram of an example computing device thatcan be used to perform one or more steps of the methods provided byexample embodiments.

DETAILED DESCRIPTION

The present disclosure relates to systems, methods, andcomputer-readable media for delineating (detecting and marking) a lesionmargin of a subject. Example systems and methods are described in detailbelow in connection with FIGS. 1-19 .

Optical coherence tomography (OCT) is a high resolution threedimensional (3D) imaging modality based on low coherence lightinterferometry. OCT has emerged as a valuable tool in many biomedicalfields, including diagnostic ophthalmology, interventional cardiology,and surgical guidance. Compared to other medical imaging modalities, OCTallows subsurface skin imaging (˜millimeter penetration depth) withmicroscopic spatial resolution and has great potential for surgicalguidance. In addition, an OCT imaging probe can be miniaturized withfiber-optic components and can be conveniently integrated into amultifunctional, hand-held surgical instrument.

Successful application of OCT in surgical guidance remains challengingdue to the following technical hurdles. First, a conventional OCT systemhas limited field of view (FOV) in both lateral and axial dimensions.Therefore, the area scanned by OCT is small and the sample must have arelatively flat surface topology to achieve an acceptable image quality.However, tumors can have significantly different lateral dimension anduneven surfaces. Therefore, a flexible scanning mechanism is needed toimage different tumors for patients. Second, it is extremely challengingif not impossible to determine malignancy through visual examination ofa massive OCT signal, because of the huge volume of data acquired (>1gigabyte (GB) data per second), varying degree of informationredundancy, and image features embedded in a variety of noise sources.Hence, there is a need to extract deep features from an OCT signal forautomatic tissue classification and tumor margin delineation. Third,critical spatial locations (tumor margin) extracted through an OCT imageanalysis are defined in the coordinate system of the specific digitalimage. To precisely guide surgical excision of tissue, there is a needfor a mechanism that registers spatial locations of interest back to thepatient.

While an example embodiment of the present disclosure is describedherein relative to Mohs micrographic surgery (MMS), exemplaryembodiments can be implemented in other types of surgeries andprocedures.

Embodiments of the present disclosure can provide systems and methods todelineate a lesion margin of a subject by integrating high-quality OCTimaging with (1) a lightweight probe that is manually and/orautomatically scanned to perform OCT imaging on uneven surfaces witharbitrary lateral FOV; (2) artificial intelligence (AI) algorithms forautomatic tissue classification and tumor margin detection; and (3) amechanism that directly registers tumor margin back to the patient.

An example method for tumor margin detection in real time and/orreal-time classification can include: (1) using experimental data(annotated images) to train a deep convolutional neural network (CNN) tosegment different layers of the tissue, such as one with a U-Netarchitecture; (2) extracting features from the segmented image fordifferent functional layers; (3) training a machine learning (ML)classifier to differentiate different tissues (normal versuspathological); (4) determining tumor margin as the location where thetissue transits from one type to another (normal to abnormal and viceversa) according to ML classification. To perform real-timeclassification margin detection, a computer software can load thepre-trained CNN and ML classifier into computer memory, can apply theCNN and ML classifier to data streaming into the computer in real-time,which provides tissue classification in real-time and identifies tumormargin as the location where the tissue transits from one type toanother.

Turning to the drawings, FIG. 1 is a diagram illustrating an exampleembodiment of a surgical marker system 100 (also referred to as system100 or intelligent surgical marker) of the present disclosure. Thesystem 100 can be embodied as a computing device 102 (e.g., thecomputing device described with respect to FIG. 19 ) in communicationwith a database 104. The computing device 102 can include, but is notlimited to, a computer system, a server, a personal computer, a cloudcomputing device, a smart phone, or any other suitable device programmedto carry out the processes disclosed herein. Still further, the system100 can be embodied as a customized hardware component such as afield-programmable gate array (“FPGA”), an application-specificintegrated circuit (“ASIC”), embedded system, or other customizedhardware components without departing from the spirit or scope of thepresent disclosure. It should be understood that FIG. 1 is just onepotential configuration, and that the system 100 of the presentdisclosure can be implemented using a number of differentconfigurations.

The database 104 includes various types of data including, but notlimited to, training OCT images, pre-trained/trained one-classclassifier, pre-trained/trained neural network for segmentation (e.g.,U-Net neural network), feature vectors, data associated with variouscomponents of the system 100 (e.g., an OCT data collection engine 110, aU-Net convolutional neural network segmentation engine 120, acontracting encoder branch 122, an expanding decoder branch 124, afeature extractor 130, a one-class classifier 140, a training engine150, an OCT optics 170, a handheld probe 180, a fiber-optic probeassembly 182, an optical fiber 184, an optical lens 186, a markerassembly 190, a marker carrier 192, and a motor(s) 194, and/or othersuitable components of the system 100).

The system 100 includes a handheld probe device 160 to capture OCTimages of a lesion area of a subject. The handheld probe device 160 caninclude, but not limited to, the OCT optics 170 and the handheld probe180. The OCT optics 170 can include various optical components (e.g.,light source, low-coherence interferometry, mirror, collimator, lens,fiber-optic coupler, spectrometer, and other suitable optics for OCTimaging). The handheld probe 180 can include, but not limited to, thefiber-optic probe assembly 182 and the marker assembly 190. Thefiber-optic probe assembly 182 is used to direct low-coherence light toa region of interest and collect light reflected from the region ofinterest. The fiber-optic probe assembly 182 can include, but notlimited to, the optical fiber 184 and the optical lens 186. The markerassembly 190 is used to create a visible mark/label on the lesion marginof the subject. The marker assembly 190 can include, but not limited to,a marker carrier 192 configured to carry biomaterial to create a visiblelabel on a tissue, and a motor(s) 194 configured to change a position ofthe marker carrier relative to the subject. Each component of thehandheld probe device 160 is further described with respect to FIGS.2A-2D.

The system 100 further includes system code 106 (non-transitory,computer-readable instructions) stored on a non-transitorycomputer-readable medium and executable by the hardware computing device102 or one or more computer systems. The system code 106 can includevarious custom-written software modules that carry out thesteps/processes described herein, and can include, but is not limitedto, the OCT data collection engine 110, the U-Net convolutional neuralnetwork segmentation engine 120, the contracting encoder branch 122, theexpanding decoder branch 124, the feature extractor 130, the one-classclassifier 140, the training engine 150. Each component of the systemcode 106 is described with respect to FIGS. 5 and 10 .

The system code 106 can be programmed using any suitable programminglanguages including, but not limited to, C, C++, C #, Java, Python, orany other suitable language. Additionally, the system code 106 can bedistributed across multiple computer systems in communication with eachother over a communications network, and/or stored and executed on acloud computing platform and remotely accessed by a computer system incommunication with the cloud platform. The system code 106 cancommunicate with the database 104, which can be stored on the samecomputer system as the system code 106, or on one or more other computersystems in communication with the system code 106.

FIG. 2A illustrates an example embodiment of the surgical marker system100 in accordance with embodiments of the present disclosure. FIG. 2Billustrates the handheld probe 180 in FIG. 2A interacting with a tissuein accordance with embodiments of the present disclosure. FIG. 2Cillustrates an example exterior of the handheld probe 180 in FIG. 2A inaccordance with embodiments of the present disclosure. FIG. 2D is aschematic view of an example embodiment of the handheld probe 180 inFIG. 2A.

As shown in FIGS. 2A-2D, the system 100 can include the handheld probedevice 160 having the OCT optics 170, the handheld probe 180 thatinterfaces with a subject (e.g., a patient), and the computing device102 for signal/data analysis and device control. The OCT optics 170 canbe an optical system to perform an OCT imaging. The OCT optics 170 caninclude a broadband source to emit light, a collimator to produceparallel light beams, a lens to focus the parallel light beams onto amirror to form a reference arm, a single mode fiber 184 to direct lightto a region of interest and a micro lens 186 to focus the light onto theregion of interest to form a sample arm, and a spectrometer to collectlight from the reference arm and the sample arm. A reflectivity profile,called an A-scan, provides in-depth cross sectional view of a tissuestructure beneath a tissue surface and contains information about thespatial dimensions and location of structures. A cross-sectionaltomogram (B-scan) can be achieved by laterally combining a series ofthese axial depth scans (A-scan).

The handheld probe 180 can include the single mode fiber 184 (in someembodiments, a multi-mode fiber can be used) to deliver light to aregion of interest and light reflected from the region of interest tothe spectrometer and the micro lens 186 to focus the light onto theregion of interest and collect light reflected from the region ofinterest. The handheld probe 180 can further include the motor 194(e.g., miniature z-motor) that actuates the marker carrier 192 carryingcontent (e.g., skin labeling biomaterial, or the like) up and downbetween a protracted position to reach a tumor margin for labeling and aretracted position to move the marker carrier back. The handheld probe180 can further include a probe holder (e.g., a probe shaft). Thehandheld probe 180 can be in communication with the computing device 102(e.g., a computer) and the OCT optics 170.

In some embodiments, the OCT optics 170 can be based on a Fourier domainOCT. The output of the broadband source (e.g., a light source providinglow-coherence light) shown in FIG. 2A can be routed by a fiber opticcirculator to the fiber-optic probe assembly 182. In some embodiments,the fiber-optic probe assembly 182 can be made by splicing a FC/APCsingle mode patch cable to a segment of bare fiber, integrating a distaltip of the optical fiber 184 with a needle and a rubber cap at its tip,and attaching the fiber-optic probe assembly 182 with a plastic handle.The metal needle shaft provides mechanical rigidity for the probe. Therubber cap ensures gentle contact between the probe and the skin, andminimizes the deformation of skin layers during scanning. The fiber tipcan be cleaved to generate a flat surface. Through Fresnel reflection,the tip of the fiber-optic probe assembly 182 provides a reference light(E_(r)). The fiber-optic probe assembly 182 also collects signal photonsfrom the sample (E_(s)). In the common path interferometer, E_(r) andE_(s) share the same probe path and interfere to extract depth resolvedinformation from the sample. Unlike a conventional OCT imaging systembased on a Michelson interferometer, the single fiber probe describedherein enables common path OCT imaging where E_(r) and E_(s) share thesame probe path. In addition, 2D images can be acquired through manualscanning. Speckle decorrelation analysis is performed to correctdistortion artifacts. Although a single fiber probe is shown, it shouldbe understood that a multi-fiber probe can be used.

The computing device 102 can receive data from the OCT optics 170 andthe handheld probe 180 and process the received data. The computingdevice 102 can further control one or more components of the OCT optics170 and the handheld probe 180 by sending instructions and/or feedbackto the one or more components of the OCT optics 170 and the handheldprobe 180.

The handheld probe 180 can acquire photons from a skin surface of asubject to interrogate a pathological status of skin tissue. The photonscan be collected and analyzed by the OCT optics 170 that streams rawdata into the computing device 102 for image reconstruction andanalysis. Based on a deep convolutional neural network trained byexperimental data, the computing device 102 can determine if thehandheld probe 180 is acquiring signal from normal or abnormal tissueand can further determine a margin 204 of a tumor based on a transitionbetween the normal tissue and the abnormal tissue. If non-margin 202 isdetected, the computing device 102 can, concurrently with the detectionof a non-margin 202, hold the marker carrier 192 in a retracted position208 at a rest location within the handheld probe where the markercarrier 192 is away from a tissue surface. If the margin 204 isdetected, the computing device 102 can control the motor 194,concurrently with detection of a margin, to move marker carrier 192 to aprotracted position 209 protruding from the handheld probe onto thedetected margin 204 to create a visible label on the margin 204.Examples for determining normal and abnormal tissues and lesion marginsare described with respect to FIGS. 5, 8A-8M, 9A-9M, 14A-14F, 16A, 16B,and 16D-16K.

In some embodiments, the single mode fiber used for OCT imaging canprotected by a ceramic ferrule that also provides mechanical rigidity.The tip of the ferrule can be covered by a rubber cap that ensuresgentle contact with the skin during image acquisition. The rubber capalso can create an axial offset between the fiber tip and the skinsurface, which is critical in preventing sensor saturation and imageartifacts. The handheld probe 180 also houses the miniature piezo motor194 that actuates the skin marker 192 to perform data driven skinlabeling. Once a margin between normal and abnormal tissue is identifiedusing methods described herein, the miniature piezo motor 194 can becommanded to translate towards the surface of the skin to the protractedposition 209, label the margin 204, and retract back into the probe tothe retracted position 208.

FIG. 3A illustrates the handheld probe 180 in FIG. 2A scanning a lesionarea in accordance with embodiments of the present disclosure. As shownin FIG. 3A, the handheld probe 180 can manually and/or automaticallyscan a region of interest 304 along a scanning direction 302. The regionof interest 304 can include a skin lesion.

FIG. 3B illustrates a margin of a lesion labeled by the handheld probe180 in FIG. 2A via various scans in accordance with embodiments of thepresent disclosure. As shown FIG. 3B, the handheld probe 180 can performmultiple scans 310A-310F and find multiple margin locations 320A-320F.The handheld probe 180 can create a visible label at each marginlocation. A contour that precisely outlines the lateral extension of thetumor can be generated after a sufficient amount of manual and/orautomatic scans is performed.

In some embodiments, if the computing device 102 determines the tissueunder the handheld probe 180 is normal, the computing device 102 cancontrol the marker assembly 190 to be inactive without creating anylabel at the tissue. If the computing device 102 determines the tissueunder the handheld probe 180 is a boundary between normal tissue anddiseased/abnormal tissue (tumor), the computing device 102 can activatethe marker assembly 190 to create a visible label at the tissue, whichcan guide a surgical excision. The handheld probe 180 can manuallyand/or automatically scan an arbitrary lateral field of view and followan uneven surface topology of a tissue (e.g., skin).

Unlike a conventional OCT imaging system, the system 100 can performlateral scanning by manually and/or automatically steering the handheldprobe 180 across a region of interest. Therefore, the imaging probe canbe extremely simple, lightweight, and low cost. A motion tracking methodcan be utilized based on a speckle decorrelation analysis, to quantifythe lateral displacement between adjacent Ascans and correct distortionartifact caused by manual scanning (e.g., when the probe is manuallyscanned, the resultant image has a nonconstant spatial sampling rateinduced by the nonconstant scanning speed). Briefly, thecross-correlation pi is calculated between sequentially acquired Ascans(S_(i) and S_(i+1)):

${\rho_{i} = \frac{\left( {\left( {S_{i} - \left( S_{i} \right)} \right)\left( {S_{i + 1} - \left( S_{i + 1} \right)} \right)} \right)}{\sigma_{i}\sigma_{i + 1}}},$

the cross-correlation coefficient is used to quantify lateraldisplacement:

${{\delta x_{i}} = {w_{0}\sqrt{\ln\left( \frac{1}{\rho_{i}} \right)}}},$

the accumulated lateral displacement is calculated x_(n)=Σ_(i=1)^(n)δx_(i), and samples an Ascan when x_(n) reaches Δx that is thepre-determined lateral sampling interval. With the above motion trackingand Ascan resampling method, the present inventors are able toreconstruct distortion free OCT images using data obtained from manualscanning. The system 100 can acquire OCT signal using a frame grabber,processes signal in real-time using a graphics processing unit (GPU),and can use a Precision workstation to coordinate data acquisition,processing, and display.

FIG. 4A schematically illustrates example scanning trajectories 400along a tumor, a surgical margin, and a normal skin. FIG. 4B is aclinical photograph of a basal cell carcinoma (BCC) tumor 402 on a rightneck imaged in a patient. FIG. 4C is an OCT image of the BCC tumor inFIG. 4B using a first scanning trajectory (12 o'clock direction) in FIG.4A. FIG. 4D is an OCT image of the BCC tumor 402 in FIG. 4B using asecond scanning trajectory (3 o'clock direction) in FIG. 4A. FIG. 4E isan OCT image of the BCC tumor 402 in FIG. 4B using a third scanningtrajectory (6 o'clock direction) in FIG. 4A. FIG. 4F is an OCT image ofthe BCC tumor 402 in FIG. 4B using a fourth scanning trajectory (9o'clock direction) in FIG. 4A. FIG. 4G is a clinical photograph of asquamous cell carcinoma (SCC) tumor 403 on a scalp imaged in a Patient.FIG. 4H is an OCT image of the SCC tumor 403 in FIG. 4G using a firstscanning trajectory (12 o'clock direction) in FIG. 4A. FIG. 4I is an OCTimage of the SCC tumor 403 in FIG. 4G using a second scanning trajectory(3 o'clock direction) in FIG. 4A. FIG. 4J is an OCT image of the SCCtumor 403 in FIG. 4G using a third scanning trajectory (6 o'clockdirection) in FIG. 4A. FIG. 4K is an OCT image of the SCC tumor 403 inFIG. 4G using a fourth scanning trajectory 4 (9 o'clock direction) inFIG. 4A. FIG. 4L is a magnified illustration of a normal tissue area inFIG. 4C. The scan trajectories described with references to FIGS. 4A-4Lare example trajectories and the numbering associated with thetrajectory is no meant to impart an order with which the trajectoriesare performed, but rather to distinguish one trajectory from a another.Additional or different trajectories can be used to detect and mark amargin between norm and abnormal skin.

To demonstrate different signal characteristics for tumor and normalskin in the same OCT image, FIGS. 4C-4K present results obtained fromtwo patients (Patient 1 with BCC and Patient 2 with SCC).Two-dimensional (2D) OCT images were obtained by manually scanning thesingle fiber probe (e.g., the handheld probe 180) across the skintumors, along trajectories indicated by arrows in FIG. 4A. Ascans weresampled with a large lateral interval (e.g., about 51 micrometers) toachieve a sufficiently large FOV and cover a center of a tumor, a tumormargin, and adjacent normal skin tissues. Each OCT scanning started fromthe center of the tumor, moved beyond the margin labeled by the surgeon,and ended at normal skin tissues. Hence, the left part of each OCT scancorresponds to the tumor indicated by bars on the left above the OCTimages in FIGS. 4C-4K. When the probe (e.g., the handheld probe 180)moved to normal tissue surrounding the tumor (e.g., right part of eachOCT image, indicated by normal-skin bars above the OCT images in FIGS.4C-4K), OCT signals showed a uniform epidermis, with a clear transitionrepresenting the dermo-epidermal junction (DEJ) 424. This can be seenmore clearly in a magnified illustration in FIG. 4L that shows a brightstratum corneum 420, homogenous medium grey epidermis 422, a darker greyDEJ 424 representing a clear transition to the underlying light greydermis 428, and dark grey-black subcutis 426. In each OCT image, regionscorresponding to the tumor (e.g., the BCC tumor 402 or the SCC tumor403) show a disrupted epidermis, losing the clear demarcation betweenthe dermis and epidermis. Moreover, manual scanning images (FIGS. 4C-4F)obtained from BCC tumor 402 also show BCC features, including plug-likestructures and upper dermis signal-free cavities (areas enclosed bydashed lines). Manual scanning images (FIGS. 4H-4K) obtained from SCCtumor 403 show SCC features, including a highly reflective surface withdiscrete bright regions below the surface (indicated by arrows).

FIG. 5 is a flowchart illustrating overall processing steps 500 carriedout by the system 100 of the present disclosure. In step 502, the system100 captures one or more OCT images using low-coherence light. Forexample, as shown in FIGS. 1, 2A-2D, 3A and 3B, the OCT collectionengine 110 can control the OCT optics 170 and/or the handheld probe 180to capture one or more OCT images. Examples of OCT images are describedwith respect to FIGS. 4A-4K, 7-9, 14, and 16 .

In step 504, the system 100 segments, by a neural network, each pixelsof the OCT image into different tissue-type categories. For example, theU-Net convolutional neural network segmentation engine 110 can segmenteach pixel of the OCT image into different tissue-type categories. Forexample, as shown in FIG. 3A, a skin tissue 306 can have a stratumcorneum category, an epidermis category, and a dermis category. Examplesegmentations using the U-Net convolutional neural network is furtherdescribed with respect to FIGS. 6, 7A-7C, 8A-8M, and 9A-9M.

In step 506, the system 100 generates one or more feature vectors basedat least in part on the segmented pixels. In some embodiments, thefeature extractor 130 can generate feature vectors using activated andsegmented OCT images. For example, the system 100 (e.g., the U-Net CNNsegmentation engine 120) forward propagates the OCT image through thetrained U-Net neural network up to a layer (e.g., an abstraction layer)prior to a segmentation layer of the U-Net neural network. The system100 can determine an activation value as a result of forward propagationfor each pixel of the OCT image corresponding to each filter. Theactivation values can be used to generate the feature vectors as furtherdescribed with respect to FIGS. 11 and 12B. In some embodiments, thesystem 100 can determine a spatial variation in thickness of a segmentedtissue structure (e.g., epidermis, dermis) associated with a particulartissue-type category based at least in part on the segmented pixels andthe spatial variation in thickness of the segmented tissue structure canbe sued to generate a feature vector as further described with respectto FIGS. 8D, 8G, 8J, 8M, 9D, 9G, 9J, 9M, and 13 .

In step 508, the system 100 determines, by a one-class classifier, aboundary location in the OCT image between a normal tissue and anabnormal tissue of the tissue structure based at least in part on theone or more feature vectors. For example, the system 100 can determine alesion margin based on prediction scores. The one-class classifier 140(e.g., one-class support vector machine (SVM) classifier) can generate aprediction score for each pixel of each of the one or more OCT images. Aprediction score indicative of the normal tissue is greater than athreshold value, and a prediction score indicative of the abnormaltissue is less than the threshold value. A threshold value refers to avalue or a range indicative of a normal tissue. In some embodiments, athreshold value can be zero. A positive score is indicative of a normaltissue, and a negative score is indicative of an abnormal tissue. Thesystem 100 can determine a pixel location as the boundary location. Thepixel location of the boundary location corresponds to where atransition occurs between the prediction score indicative of the normaltissue and the prediction score indicative of the abnormal tissue.Examples of determining lesion margins using prediction scores aredescribed with respect to FIGS. 14A-14F.

In step 510, the system 100 controls a marker assembly to selectivelycreate a visible label on a tissue location of the subject. The tissuelocation corresponds to the boundary location. For example, as shown inFIGS. 1, 2A and 2B, the marker assembly 190 includes the marker carrier192 and the motor 194. The processor can control the motor 194 to place,from a rest location 208 of the marker carrier 192, the marker carrier192 proximate to the tissue location 206 or one of 320A-320F based atleast in part on the determination of the boundary location such thatthe marker carrier 192 is activated to create the visible label on thetissue location 206 or one of 320A-320F. Subsequent to creation of thevisible label on the tissue location, the computing device 102 cancontrol the motor 194 to move the marker carrier 192 back to the restlocation 208. If the system 100 determines that a tissue under thehandheld probe 180 is a normal tissue, the computing device 102 cancontrol the motor 194 to hold the marker carrier 192 at the restlocation 208.

The system 100 can covert the boundary location from an image-basedcoordinate system (e.g., coordinate system used for OCT images) to asubject-based coordinate system (e.g., real world coordinate system or acoordinate system used for tissues and/or subjects). For example, thesystem 100 can perform an image registration to transform pixellocations on the OCT images to corresponding tissue locations on thesubject (e.g., patient) such that the system 100 can label the lesionmargin on the subject and a surgeon can extract the lesion from thesubject based on the labeled lesion margin.

FIG. 6 illustrates a U-Net architecture 600 for dermal OCT imagesegmentation. The U-Net architecture 600 allows quantitative assessmentof epidermal thickness, automatically, and with high accuracy, fornormal skin and skin lesions. As shown in FIG. 6 , the U-Netarchitecture 600 has the contracting encoder branch 122 and theexpanding decoder branch 124. The contracting encoder branch 122 hasfive stages to extract multiscale features of an input image (e.g., anOCT image captured by the handheld probe device 160) while the expandingdecoder branch 124 has five stages to generate a spatially resolvedprediction of individual pixels for segmentation. Each encoder stage hasfive layers including a 3×3 convolution layer, a rectified linearactivation function (ReLU) activation layer, a 3×3 convolution layer, anReLU activation layer, and a max pooling layer). Each decoder stage hasseven layers including an up convolution layer for upsampling, an upReLU layer, a concatenation layer, a 3×3 convolution layer, a ReLUlayer, a 3×3 convolution layer, and a ReLU layer. The U-Net architecture600 has input and output images with a dimension of 256 (axial dimensionor z dimension)×32 (lateral dimension or x dimension) for illustration.In lateral dimension, the image input into the U-Net architecture 600has 32 Ascans. It should be understood that the contracting encoderbranch 122 and the expanding decoder branch 124 can have differentnumber of stages and each stage can have different number of layers, andthe input and output images can have different dimensions.

The system 100 (e.g., U-Net convolutional neural network segmentationengine 120) can use a neural network with the U-Net architecture 600 toperform tasks including DEJ assessment (examples are described withrespect to FIGS. 7A-7C, 8A-8M, and 9A-9M) and skin layer thicknessquantification (examples are described with respect to FIGS. 8A-8M, and9A-9M).

FIG. 7A is an OCT image 700 of a skin tissue. FIG. 7B is a ground truthimage 710 providing ground truth labels for the OCT image 710 in FIG.7A. FIG. 7C is a segmented OCT image 720 having labels generated by theU-Net architecture 600 for individual pixels of the OCT image 700 inFIG. 7A.

As shown in FIGS. 7A-7C, the neural network with the U-Net architecture600 can perform DEJ assessment (also referred to as the DEJsegmentation) by generating rules to assign a label (e.g., air 712,stratum corneum 714, epidermis 716, or dermis 718) to every pixel of theOCT image 700. The neural network with the U-Net architecture 600 can betrained using image data (e.g., the OCT image 700) and ground truthpixel classification (e.g., the ground truth image 710 based on manualannotation data) to segment the OCT image 700 into layers including air712, stratum corneum 714, epidermis 716, and dermis 718 (e.g., thesegmented OCT image 720). In some embodiments, cross entropy can be usedas the loss function for the U-net neural network. For each pixel, theneural network with the U-Net architecture 600 can effectively calculatea likelihood indicative of a pixel belonging to a specific category(e.g., air 712, stratum corneum 714, epidermis 716, and dermis 718).That pixel can be assigned a category that corresponds to the highestprobability.

FIG. 8A is a photograph of a scar 800 induced by laser irradiation. FIG.8B is an OCT image of the scar 800 in FIG. 8A using a first scanningtrajectory (12 o'clock direction) in FIG. 4A. FIG. 8C is a U-Netsegmentation image of the OCT image in FIG. 8B. FIG. 8D illustrates anepidermal thickness along the lateral dimension of the OCT image in FIG.8B. FIG. 8E is an OCT image of the scar 800 in FIG. 8A using a secondscanning trajectory (3 o'clock direction) in FIG. 4A. FIG. 8F is a U-Netsegmentation image of the OCT image in FIG. 8E. FIG. 8G illustrates anepidermal thickness along the lateral dimension of the OCT image in FIG.8E. FIG. 8H is an OCT image of the scar 800 in FIG. 8A using a thirdscanning trajectory (6 o'clock direction) in FIG. 4A. FIG. 8I is a U-Netsegmentation image of the OCT image in FIG. 8H. FIG. 8J illustrates anepidermal thickness along the lateral dimension of the OCT image in FIG.8H. FIG. 8K is an OCT image of the scar 800 in FIG. 8A using a fourthscanning trajectory (9 o'clock direction) in FIG. 4A. FIG. 8L is a U-Netsegmentation image of the OCT image in FIG. 8K. FIG. 8M illustrates anepidermal thickness along the lateral dimension of the OCT image in FIG.8K.

In FIG. 8A, the scar 800 was located at a forearm of a subject. The scar800 was formed by irradiating the skin with carbon dioxide (CO₂)fractional laser (5 millijoules (mJ), density 40% laser pulses) sevendays prior to the imaging experiments. The age of the scars evaluatedcorresponds to the proliferative stage of wound healing—correlating tothe timeline when a patient would typically return for MMS of a lesionthat had recently been biopsied.

In FIG. 8B, the right side of the OCT image shows normal skin. The arrowindicates a mark 802 in FIG. 8A of a metallic ink pen. In comparison,the scar tissue 800 on the left side of the OCT image shows a thin andbright surface layer, followed by a signal void layer with significantlyreduced OCT magnitude and then a layer with increased signal magnitude.

In FIG. 8C, for normal skin 804, the layer identified as epidermis byU-Net has a slowly varying thickness along the lateral dimension. As tothe scar tissue 800, a layer identified as epidermis 716 using the U-Netarchitecture 600 has significantly different thickness along the lateraldimension.

In FIG. 8D, the thickness of the epidermis fluctuates drastically withinthe lesion and diminishes to zero at some locations, suggesting analtered skin structure associated with scarring. Results obtained fromother scanning trajectories in FIGS. 8E-8M show similar contrast betweenthe normal skin 804 and the scar 800.

FIG. 9A is a photograph of a SCC tumor 900 at a forehead of a patient.FIG. 9B is an OCT image of the SCC tumor 900 in FIG. 9A using a firstscanning trajectory (12 o'clock direction) in FIG. 4A. FIG. 9C is aU-Net segmentation image of the OCT image in FIG. 9B. FIG. 9Dillustrates an epidermal thickness along the lateral dimension of theOCT image in FIG. 9B. FIG. 9E is an OCT image of the SCC tumor 900 inFIG. 9A using a second scanning trajectory (3 o'clock direction) in FIG.4A. FIG. 9F is a U-Net segmentation image of the OCT image in FIG. 9E.FIG. 9G illustrates an epidermal thickness along the lateral dimensionof the OCT image in FIG. 9E. FIG. 9H is an OCT image of the SCC tumor900 in FIG. 9A using a third scanning trajectory (6 o'clock direction)in FIG. 4A. FIG. 9I is a U-Net segmentation image of the OCT image inFIG. 9H. FIG. 9J illustrates an epidermal thickness along the lateraldimension of the OCT image in FIG. 9H. FIG. 9K is an OCT image of theSCC tumor 900 in FIG. 9A using a fourth scanning trajectory (9 o'clockdirection) in FIG. 4A. FIG. 9L is a U-Net segmentation image of the OCTimage in FIG. 9K. FIG. 9M illustrates an epidermal thickness along thelateral dimension of the OCT image in FIG. 9K.

Results shown in FIGS. 9B-9M were obtained from the patient with the SCCtumor 900 at his forehead. Prior to imaging, the Mohs surgeon used ametallic marking pen to draw the surgical margin along which the firststage would be excised.

In FIG. 9B, the OCT image was obtained through a manual scanning alongthe 12 o'clock scanning trajectory via the system 100. The arrowindicates a mark 902 of a metallic ink pen. The right side of the OCTimage corresponds normal skin 904. In comparison, the tumor on the leftside of the OCT image shows a thin and bright surface layer. Underneaththe layer, the OCT signal decays at a faster rate, compared to the decayin the normal skin 904. This is consistent with the clinical photographin FIG. 9A that shows a tumor with a translucent appearance because ofreduced scattering.

In FIG. 9C, for the normal skin 904, ordinary skin architecture isdetected (the right side of the labeled image). For the skin tumor 900,the U-Net neural network of the system 100 classifies most pixelsunderneath the surface layer as “background” pixels, because of the lowsignal magnitude and lack of structural features.

In FIG. 9D, the thickness of epidermis determined through the U-Netsegmentation is small and varies significantly along the lateraldimension. Results obtained from other scanning trajectories in FIGS.9E-9M show similar contrast between the normal skin 904 and the SCCtumor 900.

FIG. 10 is an example flowchart illustrating steps 1000 for training apre-trained one-class classifier to differentiate abnormal tissue fromnormal skin tissue in accordance with embodiments of the presentdisclosure.

In step 1002, the system 100 (e.g., the training engine 150) receives atraining OCT image. Dataset for training and validation can have manyOCT images obtained from normal skin. However, it is much challenging toacquire images from abnormal or diseased skin, simply because of thelimited number of patients. Moreover, different pathology appearsdifferent under OCT examination. Hence, it is challenging to establish abalanced dataset to train a classifier that classifies normal skintissue, and tissues with a variety of pathologies. To overcome thischallenge, the one-class classifier of the present disclosure is trainedusing features extracted only from normal skin tissue. Training OCTimages can be a collection of OCT images, each OCT image having normaltissue. The one-class classifier learns the characteristics of normaltissue, and identifies abnormal tissue when different characteristicsfrom normal tissue are observed.

In step 1004, the system 100 forwards propagate the training OCT imagethrough the neural network up to a layer prior to layers forsegmentation. For example, features learned by the U-Net architecture600 can be used to train the pre-trained one-class classifier. Thetraining OCT image can be forward propagated through the trained U-Netnetwork up to a layer right before a segmentation layer. The U-Netarchitecture 600 can extract features from the training OCT image atdifferent scales and different abstraction layers. An example isdescribed with respect to FIG. 12A.

In step 1006, the system 100 determines an activation value as a resultof forward propagation for each pixel of the training OCT imagecorresponding to each filter. An example is described with respect toFIG. 11 .

In step 1008, the system 100 segments the training OCT image into thedifferent tissue-type categories. For example, as shown in FIGS. 6 and7C, the U-Net architecture 600 can further process the featuresextracted at different scales and different abstraction layers byperforming pixel classification at the segmentation layer. The system100 can classify each pixel into a specific tissue-type category. Forexample, the U-Net architecture 600 can classify OCT pixels intocategories including air, stratum corneum, epidermis, dermis, andbackground. Using the results of U-Net segmentation, spatially resolvedfeatures can be extracted for a specific transverse location, includingthe thickness of epidermis (v₁) and the spatial variation of epidermalthickness (v₂). A multidimensional feature (v=[v₁, v₂, v₃, . . . ]) canbe extracted from the training OCT image.

In step 1010, the system 100 generates a training feature vector basedat least in part on the activation value and the segmented training OCTimage. For example, the system 100 can average activation values foreach filter (x_(i) for the ith filter) and use results obtained from allthe filters to establish a vector x (x=[x₁, x₂, . . . , x_(N)]′ andN=16). The system 100 can feed the vector x (x=[x₁, x₂, . . . , x_(N)]′and N=16) and/or the feature (v=[v₁, v₂, v₃, . . . ]) obtained from thesegmented training OCT image into the feature extractor 130 to createthe training feature vector for training the pre-trained one-classclassifier. An example is described with respect to FIG. 12A.

In step 1012, the system 100 trains the pre-trained one-class classifierbased at least in part on the training feature vector to create atrained one-class classifier. The trained one-classifier can separatedata in the transformed high-dimensional predictor space to detectoutliers, and is effective at producing decision surfaces fromhigh-dimensional feature vectors. The one-class classifier utilizes thefacts that OCT images of normal skin are similar and OCT images of skinunder pathological conditions are different from those of normal skin tocircumvent the need to acquire images from patients with different skincancers and to train the classifier using OCT images of normal skinobtained from healthy subjects. An example is described with respect toFIG. 12A.

FIG. 11 illustrates an activation value at each pixel corresponding toeach filter in accordance with embodiments of the present disclosure. Asshown in FIG. 11 , an activation value is created as the result offorward propagation for each pixel corresponding to each filter (N=16for 16 filters). For each image patch (32 Ascans), the system 100 canaverage the activation values for each filter (x_(i) for the i^(th)filter) and use results obtained from all the filters to establish thefeature vector x (x=[x₁, x₂, . . . , x_(N)]′ and N=16).

FIG. 12A is an example data flow 1200A for training a one-classclassifier in accordance with embodiments of the present disclosure.FIG. 12B is an example data flow 1200B for applying the trainedone-class classifier from FIG. 12A to an incoming OCT image.

As shown in FIG. 12A, an training image 1202A is input into forwardpropagation layers in U-Net 1204A. An output 1208A (e.g., the resultsshown in FIG. 11 ) from the forward propagation layers in U-Net 1204A isinput into a U-Net segmentation layer 1210A and a feature extraction1212A. An output 1206A (e.g., the results shown in FIG. 7C) from theU-Net segmentation layer 1210A is input into the feature extraction1212A. The feature extraction 1212A generates feature vectors fortraining a SVM classifier. With the SVM classifier trained, an incomingOCT image 1202B is activated by a forward propagation layers in U-Net1204B and segmented by a U-Net segmentation layer 1210B to create afeature vector by a feature extraction 1212B. The feature vector isinput into the trained SVM classifier to classify tissue in the incomingOCT image 1202B as normal or abnormal 1216. One-class SVM classifier isused in FIGS. 12A and 12B. It should be understood that a differentone-class classifier can be used.

FIG. 13 is a graphical depiction showing an identification of supportvectors to detect outlier and skin abnormity using a combination ofepidermal thickness and the standard variation of epidermal thickness.As shown in FIG. 13 , the support vectors obtained using the combinationof epidermal thickness and the standard variation of epidermal thickness(e.g., the vector v described with respect to FIG. 10 ) are consistentwith the observation points, which indicates that the support vectorscan represent an outlier of abnormal tissue.

Example Experimental Results

To train the U-Net and the one-class SVM classifier, the OCT imagingplatform described in FIGS. 1 and 2A-2D were used to obtain images from6 healthy subjects. From each subject, the right forearm, left forearm,forehead, neck and palm were scanned. The age of the subjects rangedfrom 24 to 59. The skin type of the subjects ranged from type II to typeIV. Both male and female subjects were measured. Images that had lowquality were excluded and a training data set with 32 images wasestablished. Each image had a dimension of 2048 (Ascan number) by 256(pixel number in each Ascan). Pixels of the images were manually labeledto be air (signal free region), stratum corneum, epidermis, and dermis.The images along with the ground truth (results of manual labeling) weredivided into smaller patches (32 Ascan per patch), resulting in 2048image patches.

FIG. 14A is an OCT image obtained by scanning a fiber-optic probe acrossa junction between skin and a nail plate from a healthy subject. FIG.14B is a ground truth labeling of normal skin and nail plate (consideredas abnormal skin). FIG. 14C is an image showing features extracted fromdermis pixels at different spatial locations. FIG. 14D illustrates aplot of prediction score outputs from the trained SVM before and afterlow-pass filtering. FIG. 14E is an image of abnormal skin identified bya one-class classifier without filtering the prediction scores. FIG. 14Fis an image abnormal skin identified by the one-class classifier afterfiltering the prediction scores.

To validate that the one-class classifier allowed spatially resolvedtissue classification, the fiber-optic OCT probe was scanned at thethumb of a healthy subject, across the junction between the skin and thenail plate. The image obtained is shown in FIG. 14A. The left side ofthe image corresponds to the skin and the right side of the imagecorresponds to the nail plate, as shown in FIG. 14B. The signal obtainedfrom the nail was different from OCT signal of the skin and wasconsidered as abnormal. FIG. 14C shows the feature vectors at differentlateral locations. These feature vectors (x_(d)) were obtained frompixels labeled as dermis by the U-Net. Using these feature vectors andthe classifier trained by normal skin images, the system 100 was able todetermine the tissue type at different spatial locations (in FIG. 14D).The prediction scores 1410 can be spatially smoothed (e.g., 5^(th) orderButterworth filter, 0.005 cut-off frequency), resulting in the filteredcurve 1420 in FIG. 14D. The abnormal skin identified by the classifierare labeled in FIGS. 14E and 14F, using the prediction scores before(unfiltered prediction scores 1410) and after filtering (filteredprediction scores 1420). Result in FIG. 14F suggests that one-class SVMusing features extracted from dermis allowed tissue classification(normal and abnormal skin tissue) with spatial resolution.

As described previously, the features are obtained by forwardpropagating the input image through the network and averaging theactivations at the layer before segmentation. To obtain an effectivefeature vector, pixels that are overwhelmed by noise and labeled as“air” by the U-Nets have to be eliminated for feature extraction.Furthermore, features are extracted by averaging the activations among aspecific type of pixel, because different pixel types correspond todifferent activation values and averaging cross different pixel typeswill lead to suboptimal classification.

To validate our feature selection strategy, 50% of the images in thedata set for normal skin for feature extraction and one-classclassification training were used. Vectors were obtained, includingx_(all) by averaging activation values for all the pixels withoutdiscriminating the pixel type, x_(e) by averaging activation values forepidermis pixels, and x_(d) by averaging activation values for dermispixels. Stratum corneum pixels were not considered, because very fewpixels were classified as stratum corneum. The SVM was trained usingx_(all), x_(e), x_(d), and x_(e&d)=[x_(e);x_(d)], using a Gaussiankernel function and an outlier-fraction of 10%, and obtained fourdifferent classifiers, SVM_(all), SVM_(e), SVM_(d), and SVM_(e&d).

To validate the effectiveness of these classifiers, a validation dataset that included the remaining (50%) images in the data set of normalskin and computer synthesized abnormal images were created. Based on thefact that BCCs create upper dermis signal-free cavities with reduced OCTsignal magnitude, abnormal images by reducing the signal amplitude to75% of its original value from a random depth within the dermis werecreated. For each image within the validation data set, a label (normalor abnormal) was obtained, and feature vectors x_(all), x_(e), x_(d),and x_(e&d)=[x_(e);x_(d)] were calculated. The method described hereinwere able to predict the tissue type using classifiers obtained from thetraining process and evaluate the accuracy of classification.

FIG. 15 illustrates receiver operating characteristic (ROC) curves 1500for different classifiers obtained using a validating data set withnormal skin images and computer synthesized abnormal images. SVM_(d)1502 has the largest area under curve (AUC) value compared with othertissue types. Table 1 shows assessment of classification accuracy whenthe classifiers were trained with an outlier ratio of 10%.

TABLE 1 Sensitivity Specificity Accuracy SVM_(e&d) 0.78 0.85 0.81SVM_(e) 0.52 0.85 0.69 SVM_(d) 0.87 0.85 0.86 SVM_(all) 0.62 0.88 0.75

In a pilot imaging experiments, a patient with basal cell carcinoma(BCC) (superficial and nodular type) was imaged. The patient was a73-year-old male and the tumor was located at his left cheek. Thesurgeon labeled the tumor with a marker. Three sets of scans wereperformed.

FIGS. 16A and 16B are OCT images of normal tissue from a patient havinga basal cell carcinoma tumor. The normal skin at forearm of the patientwas scanned and images were obtained. Similar to other images obtainedfrom normal skin, FIGS. 16A and 16B have clearly visibledermis-epidermis junction (DEJ). The first layer of the skin (stratumcorneum) is thin and bright, followed by epidermis with reducedbrightness. Underneath is dermis where the signal decreases as depth. Toperform automatic tissue assessment, the image obtained from normalforearm skin (256 Ascans) was divided into eight non-overlapping patches(32 Ascans). Following procedures illustrated in FIG. 12B to processeach image patch, the U-Net was used to segment the image into differentskin layers, extracted feature vectors and used the trained classifierto determine whether the tissue was normal or abnormal. For an inputfeature vector, the classifier output a prediction value. A positiveprediction value corresponded to normal skin tissue, while a negativeprediction value corresponded to abnormal. The average prediction valuescalculated using all the image patches are 1.06 and 0.54, respectively.These values implied that the skin scanned was normal. This wasconsistent with the fact that we obtained FIGS. 16A and 16B from normalskin.

FIG. 16C schematically illustrates scanning trajectories 1600A-1600H forscanning a tumor 1604 of the same patient in FIG. 16A. FIG. 16D is anOCT image by scanning a tumor of the same patient in FIG. 16A along afirst scanning trajectory 1600A in FIG. 16C. FIG. 16E is an OCT image byscanning the tumor along a second scanning trajectory 1600B in FIG. 16C.FIG. 16F is an OCT image by scanning the tumor in FIG. 16A along a thirdscanning trajectory 1600C in FIG. 16C. FIG. 16G is an OCT image byscanning the tumor along a fourth scanning trajectory 1600D in FIG. 16C.

Compared to the normal skin from the same patient, images obtained fromthe tumor features disruption of DEJ and reduced OCT signal amplitudestarting from upper dermis. To validate the automatic tissuecharacterization approach described herein, the procedures described inFIG. 12B were followed and negative prediction values for all the imagesobtained from the tumor were obtained. The average prediction values forall the images were negative, suggesting the region scanned correspondedto abnormal skin tissue.

FIG. 16H is an OCT image by scanning a tissue region along the scanninga fifth trajectory 1600E in FIG. 16C. FIG. 16I is an OCT image byscanning the tissue region along a sixth scanning trajectory 1600F inFIG. 16C. FIG. 16J is an OCT image by scanning the tissue region along aseventh scanning trajectory 1600G in FIG. 16C. FIG. 16K is an OCT imageby scanning tissue region along an eighth scanning trajectory 1600H inFIG. 16C. FIG. 17 is an histologic image 1700 of scanning areas in FIGS.16D-16K. The scanning trajectories 1600E-1600H were across a surgeonoutline 1602. The average prediction scores for FIGS. 16H and 16I werenegative, while the average prediction scores for FIGS. 16J and 16K werepositive. This is confirmed by histology 1700 in FIG. 17 . Table 2 showsprediction scores and results for FIGS. 16A-16K.

TABLE 2 SVM score Normal or abnormal Normal skin (forearm 4.2 NormalScan1) Normal skin (forearm 2.1 Normal Scan2) Tumor (scan 1) −5.3Abnormal Tumor (scan 2) −3.2 Abnormal Tumor (scan 3) −5.3 Abnormal Tumor(scan 4) −5.3 Abnormal Margin (scan 5) −2.3 Abnormal (positive margin)Margin (scan 6) −4.8 Abnormal (positive margin) Margin (scan 7) 2.9Normal (negative margin) Margin (scan 8) 6.5 Normal (negative margin)

The scan trajectories described with references to FIGS. 16A-16K areexample trajectories and the numbering associated with the trajectory isno meant to impart an order with which the trajectories are performed,but rather to distinguish one trajectory from a another. Additional ordifferent trajectories can be used to detect and mark a margin betweennorm and abnormal skin.

FIG. 18 an example diagram illustrating computer hardware and networkcomponents on which the system 1800 can be implemented. The system 1800can include a plurality of computation servers 1802 a-1802 n having atleast one processor (e.g., one or more graphics processing units (GPUs),microprocessors, central processing units (CPUs), tensor processingunits (TPUs), application-specific integrated circuits (ASICs), etc.)and memory for executing the computer instructions and methods describedabove (which can be embodied as system code 106). The system 1800 canalso include a plurality of data storage servers 1804 a-1804 n forstoring data. The computation servers 1802 a-1802 n, the data storageservers 1804 a-1804 n, and the user device 1810 can communicate over acommunication network 1808. Of course, the system 1800 need not beimplemented on multiple devices, and indeed, the system 1800 can beimplemented on a single (e.g., a personal computer, server, mobilecomputer, smart phone, etc.) without departing from the spirit or scopeof the present disclosure.

FIG. 19 is an example block diagram of an example computing device 102that can be used to perform one or more steps of the methods provided byexample embodiments. The computing device 102 includes one or morenon-transitory computer-readable media for storing one or morecomputer-executable instructions or software for implementing exampleembodiments. The non-transitory computer-readable media can include, butare not limited to, one or more types of hardware memory, non-transitorytangible media (for example, one or more magnetic storage disks, one ormore optical disks, one or more USB flashdrives), and the like. Forexample, memory 1906 included in the computing device 102 can storecomputer-readable and computer-executable instructions or software forimplementing example embodiments. The computing device 102 also includesprocessor 1902 and associated core 1904, and optionally, one or moreadditional processor(s) 1902′ and associated core(s) 1904′ (for example,in the case of computer systems having multiple processors/cores), forexecuting computer-readable and computer-executable instructions orsoftware stored in the memory 1906 and other programs for controllingsystem hardware. Processor 1902 and processor(s) 1902′ can each be asingle core processor or multiple core (1904 and 1904′) processor. Thecomputing device 102 also includes a graphics processing unit (GPU)1905. In some embodiments, the computing system 102 includes multipleGPUs.

Virtualization can be employed in the computing device 102 so thatinfrastructure and resources in the computing device can be shareddynamically. A virtual machine 1914 can be provided to handle a processrunning on multiple processors so that the process appears to be usingonly one computing resource rather than multiple computing resources.Multiple virtual machines can also be used with one processor.

Memory 1906 can include a computer system memory or random accessmemory, such as DRAM, SRAM, EDO RAM, and the like. Memory 1906 caninclude other types of memory as well, or combinations thereof. A usercan interact with the computing device 102 through a visual displaydevice 1918, such as a touch screen display or computer monitor, whichcan display one or more user interfaces 1919. The visual display device1918 can also display other aspects, transducers and/or information ordata associated with example embodiments. The computing device 102 caninclude other I/O devices for receiving input from a user, for example,a keyboard or any suitable multi-point touch interface 1908, a pointingdevice 1910 (e.g., a pen, stylus, mouse, or trackpad). The keyboard 1908and the pointing device 1910 can be coupled to the visual display device1918. The computing device 102 can include other suitable conventionalI/O peripherals.

The computing device 102 can also include one or more storage devices1924, such as a hard-drive, CD-ROM, or other computer readable media,for storing data and computer-readable instructions, applications,and/or software that implements example operations/steps of the system(e.g., the systems 100 and 1000) as described herein, or portionsthereof, which can be executed to generate user interface 1919 ondisplay 1918. Example storage device 1924 can also store one or moredatabases for storing any suitable information required to implementexample embodiments. The databases can be updated by a user orautomatically at any suitable time to add, delete or update one or moreitems in the databases. Example storage device 1924 can store one ormore databases 1926 for storing provisioned data, and otherdata/information used to implement example embodiments of the systemsand methods described herein.

The system code 106 as taught herein may be embodied as an executableprogram and stored in the storage 1924 and the memory 1906. Theexecutable program can be executed by the processor to perform thein-situ inspection as taught herein.

The computing device 102 can include a network interface 1912 configuredto interface via one or more network devices 1922 with one or morenetworks, for example, Local Area Network (LAN), Wide Area Network (WAN)or the Internet through a variety of connections including, but notlimited to, standard telephone lines, LAN or WAN links (for example,802.11, T1, T3, 56 kb, X.25), broadband connections (for example, ISDN,Frame Relay, ATM), wireless connections, controller area network (CAN),or some combination of any or all of the above. The network interface1912 can include a built-in network adapter, network interface card,PCMCIA network card, card bus network adapter, wireless network adapter,USB network adapter, modem or any other device suitable for interfacingthe computing device 102 to any type of network capable of communicationand performing the operations described herein. Moreover, the computingdevice 102 can be any computer system, such as a workstation, desktopcomputer, server, laptop, handheld computer, tablet computer (e.g., theiPad® tablet computer), mobile computing or communication device (e.g.,the iPhone® communication device), or other form of computing ortelecommunications device that is capable of communication and that hassufficient processor power and memory capacity to perform the operationsdescribed herein.

The computing device 102 can run any operating system 1916, such as anyof the versions of the Microsoft® Windows® operating systems, thedifferent releases of the Unix and Linux operating systems, any versionof the MacOS® for Macintosh computers, any embedded operating system,any real-time operating system, any open source operating system, anyproprietary operating system, any operating systems for mobile computingdevices, or any other operating system capable of running on thecomputing device and performing the operations described herein. In someembodiments, the operating system 1916 can be run in native mode oremulated mode. In some embodiments, the operating system 1916 can be runon one or more cloud machine instances.

It should be understood that the operations and processes describedabove and illustrated in the figures can be carried out or performed inany suitable order as desired in various implementations. Additionally,in certain implementations, at least a portion of the operations can becarried out in parallel. Furthermore, in certain implementations, lessthan or more than the operations described can be performed.

In describing example embodiments, specific terminology is used for thesake of clarity. For purposes of description, each specific term isintended to at least include all technical and functional equivalentsthat operate in a similar manner to accomplish a similar purpose.Additionally, in some instances where a particular example embodimentincludes multiple system elements, device components or method steps,those elements, components or steps may be replaced with a singleelement, component or step. Likewise, a single element, component orstep may be replaced with multiple elements, components or steps thatserve the same purpose. Moreover, while example embodiments have beenshown and described with references to particular embodiments thereof,those of ordinary skill in the art will understand that varioussubstitutions and alterations in form and detail may be made thereinwithout departing from the scope of the present disclosure. Furtherstill, other embodiments, functions and advantages are also within thescope of the present disclosure.

While exemplary embodiments have been described herein, it is expresslynoted that these embodiments should not be construed as limiting, butrather that additions and modifications to what is expressly describedherein also are included within the scope of the invention. Moreover, itis to be understood that the features of the various embodimentsdescribed herein are not mutually exclusive and can exist in variouscombinations and permutations, even if such combinations or permutationsare not made express herein, without departing from the spirit and scopeof the invention.

What is claimed is:
 1. A surgical marker system for delineating a lesionmargin in tissue of a subject, the surgical marker system comprising: ahandheld probe device configured to capture an optical coherencetomography (OCT) image, the OCT image providing an in-depth crosssectional view of a tissue structure beneath a tissue surface, thehandheld probe device comprising a handheld probe including: afiber-optic probe assembly configured to direct low-coherence light to aregion of interest and collect light reflected from the region ofinterest to capture the OCT image; and a marker assembly configured toselectively create a visible label on the lesion margin of the subject;and a processor coupled to a memory, the processor configured to:segment, by a neural network, each pixel of the OCT image into differenttissue-type categories; generate one or more feature vectors based atleast in part on the segmented pixels; determine, by a one-classclassifier, a boundary location in the OCT image between a normal tissueand an abnormal tissue of the tissue structure based at least in part onthe one or more feature vectors; and control the marker assembly toselectively create the visible label on a tissue location of thesubject, the tissue location corresponding to the boundary location. 2.The surgical marker system of claim 1, wherein the processor is furtherconfigured to covert the boundary location from an image-basedcoordinate system to a subject-based coordinate system.
 3. The surgicalmarker system of claim 1, wherein determining the boundary locationbetween the normal tissue and the abnormal tissue of the tissuestructure comprises: generating, by the one-class classifier, aprediction score for each pixel of the OCT image, wherein a predictionscore indicative of the normal tissue is greater than a threshold value,and a prediction score indicative of the abnormal tissue is less thanthe threshold value; and determining a pixel location as the boundarylocation, the pixel location corresponding to where a transition occursbetween the prediction score indicative of the normal tissue and theprediction score indicative of the abnormal tissue.
 4. The surgicalmarker system of claim 1, wherein the processor is further configuredto: forward propagate the OCT image through the neural network up to alayer prior to a segmentation layer of the neural network; and determinean activation value as a result of forward propagation for each pixel ofthe OCT image corresponding to each filter, wherein generating the oneor more feature vectors is further based at least in part on theactivation value.
 5. The surgical marker system of claim 1, wherein theprocessor is further configured to: determining a spatial variation inthickness of a segmented tissue structure associated with a particulartissue-type category based at least in part on the segmented pixels,wherein generating the one or more feature vectors is further based atleast in part on the spatial variation in thickness of the segmentedtissue structure.
 6. The surgical marker system of claim 1, wherein theprocessor is further configured to: receive a training OCT image fortraining a pre-trained one-class classifier to create the one-classclassifier; forward propagate the training OCT image through the neuralnetwork up to a layer prior to a segmentation layer of the neutralnetwork; determine an activation value as a result of forwardpropagation for each pixel of the training OCT image corresponding toeach filter; segment, using the segmentation layer, the training OCTimage into the different tissue-type categories; generate a trainingfeature vector based at least in part on the activation value and thesegmented training OCT image; and train the pre-trained one-classclassifier based at least in part on the training feature vector tocreate the one-class classifier.
 7. The surgical marker system of claim6, wherein the training OCT image corresponds to normal tissues, and theone-class classifier is trained to recognize the normal tissues.
 8. Thesurgical marker system of claim 1, wherein each of the one or more OCTimages is captured by manually or automatically moving the fiber-opticprobe assembly across the region of interest with a first arbitrarylateral field of view.
 9. The surgical marker system of claim 1, whereinthe marker assembly comprises a marker carrier and a motor, the motorconfigured to change a position of the marker carrier relative to thesubject, wherein controlling the marker assembly to create the visiblelabel on the tissue location of the subject comprises: controlling themotor to place, from a rest location of the marker carrier, the markercarrier proximate to the tissue location based at least in part on thedetermination of the boundary location such that the marker carrier isactivated to create the visible label on the tissue location; andsubsequent to creation of the visible label on the tissue location,controlling the motor to move the marker carrier back to the restlocation.
 10. The surgical marker system of claim 1, wherein the neuralnetwork is U-Net convolution neural network.
 11. The surgical markersystem of claim 1, wherein the one-class classifier is one-class supportvector machine (SVM) classifier.
 12. The surgical marker system of claim1, wherein the tissue structure is a skin structure, and the differenttissue-type categories comprise a stratum corneum category, an epidermiscategory, and a dermis category.
 13. A method for delineating a lesionmargin of a subject, the method comprising: capturing an opticalcoherence tomography (OCT) image using low-coherence light, the OCTimage providing an in-depth cross sectional view of a tissue structurebeneath a tissue surface; segmenting, by a neural network, each pixel ofthe OCT image into different tissue-type categories; generating one ormore feature vectors based at least in part on the segmented pixels;determining, by a one-class classifier, a boundary location in the OCTimage between a normal tissue and an abnormal tissue of the tissuestructure based at least in part on the one or more feature vectors; andcontrolling a marker assembly to selectively create a visible label on atissue location of the subject, the tissue location corresponding to theboundary location.
 14. The method of claim 13, further comprising:converting the boundary location from an image-based coordinate systemto a subject-based coordinate system.
 15. The method of claim 13,wherein determining the boundary location between the normal tissue andthe abnormal tissue of the tissue structure comprises: generating, bythe one-class classifier, a prediction score for each pixel of the OCTimage, wherein a prediction score indicative of the normal tissue isgreater than a threshold value, and a prediction score indicative of theabnormal tissue is less than the threshold value; and determining apixel location as the boundary location, the pixel locationcorresponding to where a transition occurs between the prediction scoreindicative of the normal tissue and the prediction score indicative ofthe abnormal tissue.
 16. The method of claim 13, further comprising:forward propagating the OCT image through the neural network up to alayer prior to a segmentation layer of the neural network; anddetermining an activation value as a result of forward propagation foreach pixel of the OCT image corresponding to each filter, whereingenerating the one or more feature vectors is further based at least inpart on the activation value.
 17. The method of claim 13, furthercomprising: determining a spatial variation in thickness of a segmentedtissue structure associated with a particular tissue-type category basedat least in part on the segmented pixels, wherein generating the one ormore feature vectors is further based at least in part on the spatialvariation in thickness of the segmented tissue structure.
 18. The methodof claim 13, further comprising: receiving a training OCT image fortraining a pre-trained one-class classifier to create the one-classclassifier; forward propagating the training OCT image through theneural network up to a layer prior to a segmentation layer of theneutral network; determining an activation value as a result of forwardpropagation for each pixel of the training OCT image corresponding toeach filter; segmenting, using the segmentation layer, the training OCTimage into the different tissue-type categories; generating a trainingfeature vector based at least in part on the activation value and thesegmented training OCT image; and training the pre-trained one-classclassifier based at least in part on the training feature vector tocreate the one-class classifier.
 19. The method of claim 18, wherein thetraining OCT image corresponds to normal tissues, and the one-classclassifier is trained to recognize the normal tissues.
 20. The method ofclaim 13, wherein the marker assembly comprises a marker carrier and amotor, the motor configured to change a position of the marker carrierrelative to the subject, wherein controlling the marker assembly tocreate the visible label on the tissue location comprises: controllingthe motor to place, from a rest location of the marker carrier, themarker carrier proximate to the tissue location based at least in parton the determination of the boundary location such that the markercarrier is activated to create the visible label on the tissue location;and subsequent to creation of the visible label on the tissue location,controlling the motor to move the marker carrier back to the restlocation.